Exact local computed tomography based on compressive sampling

ABSTRACT

A system and method for tomographic image reconstruction using truncated projection data that allows exact interior reconstruction (interior tomography) of a region of interest (ROI) based on the known sparsity models of the ROI, thereby improving image quality while reducing radiation dosage. In addition, the method includes parallel interior tomography using multiple sources beamed at multiple angles through an ROI and that enables higher temporal resolution.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to U.S. Provisional Application Ser.No. 61/169,577, filed Apr. 15, 2009. The complete content of thatapplication is herein incorporated by reference.

STATEMENT OF GOVERNMENT INTEREST

This invention was made with government support under Grant Nos.EB002667, EB004287 and EB007288 awarded by National Institutes ofHealth. The Government has certain rights in the invention.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to x-ray computed tomography (CT) and,more particularly, to systems and methods for theoretically exactinterior reconstruction using compressive sampling (or compressedsensing or under another name for the essentially same thing) technologythat is based an appropriate signal sparsity model, with the extensionof such techniques to other tomographic modalities, such as PET/SPECT,MRI, and others that use imaging geometries of straight rays or nearlystraight rays.

2. Background Description

One conventional wisdom is that the interior problem (exactreconstruction of an interior ROI only from data associated with linesthrough the ROI) does not have a unique solution (see F. Natterer, Themathematics of computerized tomography. Classics in applied mathematics2001, Philadelphia: Society for Industrial and Applied Mathematics).Nevertheless, it is highly desirable to perform interior reconstructionfor radiation dose reduction and other important reasons. Hence, overpast years a number of image reconstruction algorithms were developedthat use an increasingly less amount of raw data (D. L. Parker, Optimalshort scan convolution reconstruction for fanbeam ct. Med. Phys., 1982.9(2): p. 254-257; F. Noo, R. Clackdoyle, and J. D. Pack, A two-stephilbert transform method for 2d image reconstruction. Physics inMedicine and Biology, 2004. 49(17): p. 3903-3923; M. Defrise, et al.,Truncated hilbert transform and image reconstruction from limitedtomographic data. Inverse Problems, 2006. 22(3): p. 1037-1053).Specifically, motivated by the major needs in cardiac CT, CT guidedprocedures, nano-CT and so on (G. Wang, Y. B Ye, and H. Y Yu, Interiortomography and instant tomography by reconstruction from truncatedlimited-angle projection data, U.S. Pat. No. 7,697,658, Apr. 13, 2010),by analytic continuation we proved that the interior problem can beexactly and stably solved if a sub-region in an ROI within afield-of-view (FOV) is known (see Y. B. Ye, et al., A general localreconstruction approach based on a truncated hilbert transform.International Journal of Biomedical Imaging, 2007, Article ID: 63634, 8pages; Y. B. Ye, H. Y. Yu, and G. Wang, Exact interior reconstructionwith cone-beam CT. International Journal of Biomedical Imaging, 2007,Article ID: 10693, 5 pages; Y. B. Ye, H. Y. Yu, and G. Wang, Exactinterior reconstruction from truncated limited-angle projection data.International Journal of Biomedical Imaging, 2008 ID: 427989, 6 Pages;H. Y. Yu, Y. B. Ye, and G. Wang, Local reconstruction using thetruncated hilbert transform via singular value decomposition. Journal ofX-Ray Science and Technology, 2008. 16(4): p. 243-251). Similar resultswere also independently reported by others (see H. Kudo, et al., Tiny apriori knowledge solves the interior problem in computed tomography.Phys. Med. Biol., 2008. 53(9): p. 2207-2231; M. Courdurier, et al.,Solving the interior problem of computed tomography using a prioriknowledge. Inverse Problems, 2008, Article ID 065001, 27 pages.).Although the CT numbers of certain sub-regions such as air in a tracheaand blood in an aorta can be indeed assumed, how to obtain preciseknowledge of a sub-region generally can be difficult in some cases suchas in studies on rare fossils or certain biomedical structures.Therefore, it would be very valuable to develop more powerful interiortomography techniques.

Another conventional wisdom is that data acquisition should be based onthe Nyquist sampling theory, which states that to reconstruct aband-limited signal or image, the sampling rate must at least double thehighest frequency of nonzero magnitude. Very interestingly, analternative theory of compressive sampling (or compressed sensing orunder another name for the essentially same thing) (CS) has recentlyemerged which shows that high-quality signals and images can bereconstructed from far fewer data/measurements than what is usuallyconsidered necessary according to the Nyquist sampling theory (see D. L.Donoho, Compressed sensing. IEEE Transactions on Information Theory,2006. 52(4): p. 1289-1306; E. J. Candes, J. Romberg, and T. Tao, Robustuncertainty principles: Exact signal reconstruction from highlyincomplete frequency information. IEEE Transactions on InformationTheory, 2006. 52(2): p. 489-509). The main idea of CS is that mostsignals are sparse in an appropriate domain (an orthonormal system ormore generally a frame system), that is, a majority of theircoefficients are close or equal to zero, when represented in thatdomain. Typically, CS starts with taking a limited amount of samples ina much less correlated basis, and the signal is exactly recovered withan overwhelming probability from the limited data via the L1 normminimization (or minimization of another appropriate norm). Sincesamples are limited, the task of recovering the image would involvesolving an underdetermined matrix equation, that is, there is a hugeamount of candidate images that can all fit the limited measurementseffectively. Thus, some additional constraint is needed to select the“best” candidate. While the classical solution to such problems is tominimize the L2 norm, the recent CS results showed that finding thecandidate with the minimum L1 norm, also basically equivalent to thetotal variation (TV) minimization in some cases (L. L. Rudin, S. Osher,and E. Fatemi, Nonlinear total variation based noise removal algorithms.Physica D, 1992. 60(1-4): p. 259-268), is a reasonable choice, and canbe expressed as a linear program and solved efficiently using existingmethods (see D. L. Donoho, Compressed sensing. IEEE Transactions onInformation Theory, 2006. 52(4): p. 1289-1306; E.J. Candes, J. Romberg,and T. Tao, Robust uncertainty principles: Exact signal reconstructionfrom highly incomplete frequency information. IEEE Transactions onInformation Theory, 2006. 52(2): p. 489-509).

Because the x-ray attenuation coefficient often varies mildly within ananatomical component, and large changes are usually confined aroundborders of tissue structures, the discrete gradient transform (DGT) hasbeen widely utilized as a sparsifying operator in CS-inspired CTreconstruction (for example, E. Y. Sidky, C. M. Kao, and X. H. Pan,Accurate image reconstruction from few-views and limited-angle data indivergent-beam ct. Journal of X-Ray Science and Technology, 2006. 14(2):p. 119-139; G. H. Chen, J. Tang, and S. Leng, Prior image constrainedcompressed sensing (piccs): A method to accurately reconstruct dynamicct images from highly undersampled projection data sets. MedicalPhysics, 2008. 35(2): p. 660-663; H. Y. Yu, and G. Wang, Compressedsensing based interior tomography. Phys Med Biol, 2009. 54(9): p.2791-2805; J. Tang, B. E. Nett, and G. H. Chen, Performance comparisonbetween total variation (tv)-based compressed sensing and statisticaliterative reconstruction algorithms. Physics in Medicine and Biology,2009. 54(19): p. 5781-5804). This kind of algorithms can be divided intotwo major steps. In the first step, an iteration formula (e.g. SART) isused to update a reconstructed image for data discrepancy reduction. Inthe second step, a search method (e.g. the standard steepest descenttechnique) is used in an iterative framework for TV minimization. Thesetwo steps need to be iteratively performed in an alternating manner.However, there are no standard stopping and parameter selection criteriafor the second step. Usually, these practical issues are addressed in anad hoc fashion.

On the other hand, soft-threshold nonlinear filtering (see M. A. T.Figueiredo, and R. D. Nowak, An em algorithm for wavelet-based imagerestoration. IEEE Transactions on Image Processing, 2003. 12(8): p.906-916; I. M. Daubechies, M. Defrise, and C. De Mol, An iterativethresholding algorithm for linear inverse problems with a sparsityconstraint. Communications on Pure and Applied Mathematics, 2004.57(11): p. 1413-1457; I. M. Daubechies, M. Fornasier, and I. Loris,Accelerated projected gradient method for linear inverse problems withsparsity constraints. Journal of Fourier Analysis and Applications,2008. 14(5-6): p. 764-792) was proved to be a convergent and efficientalgorithm for the norm minimization regularized by a sparsityconstraint. Unfortunately, because the DGT is not invertible, it doesnot satisfy the restricted isometry property (RIP) required by the CStheory (see D. L. Donoho, Compressed sensing. IEEE Transactions onInformation Theory, 2006. 52(4): p. 1289-1306; E.J. Candes, J. Romberg,and T. Tao, Robust uncertainty principles: Exact signal reconstructionfrom highly incomplete frequency information. IEEE Transactions onInformation Theory, 2006. 52(2): p. 489-509) and soft-thresholdalgorithm (see I. M. Daubechies, M. Defrise, and C. De Mol, An iterativethresholding algorithm for linear inverse problems with a sparsityconstraint. Communications on Pure and Applied Mathematics, 2004.57(11): p. 1413-1457; I. M. Daubechies, M. Fornasier, and I. Loris,Accelerated projected gradient method for linear inverse problems withsparsity constraints. Journal of Fourier Analysis and Applications,2008. 14(5-6): p. 764-792). In other words, the soft-threshold algorithmcannot be directly applied for TV minimization. The above problem can beovercome using an invertible sparsifying transform such as a wavelettransform for image compression. For an object of interest such as amedical image, we can find an orthonormal basis (or more generally, aframe) to make the object sparse in terms of significant transformcoefficients. Then, we can perform image reconstruction from a limitednumber of non-truncated or truncated projections by minimizing thecorresponding L1 norm.

Inspired by the CS theory, we proved and demonstrated the interiortomography is feasible in the CS framework assuming a sparsity model;specifically a piecewise-constant image model and a number of high-ordermodels (see H. Y. Yu, and G. Wang, Compressed sensing based interiortomography. Phys Med Biol, 2009. 54(9): p. 2791-2805; H. Y. Yu, et al.,Supplemental analysis on compressed sensing based interior tomography.Phys Med Biol, 2009. 54(18): p. N425-N432; W. M. Han, H. Y. Yu and G.Wang; A general total variation minimization theorem for compressedsensing based interior tomography; International Journal of BiomedicalImaging, Article ID: 125871, 2009, 3 pages; H. Y. Yu, et al; Compressivesampling based interior tomography for dynamic carbon nanotube Micro-CT;Journal of X-ray Science and Technology, 17(4): 295-303, 2009). Theabove finding has been extended to interior SPECT reconstructionassuming a piecewise-polynominial image model and introducing ahigh-order total variation concept (H. Y. Yu, J. S. Yang, M. Jiang, G.Wang: Methods for Exact and Approximate SPECT/PET InteriorReconstruction. VTIP No.: 08-120, U.S. Patent Application No.61/257,443, Date Filed: Nov. 2, 2009), CT is a special case of SPECTwhen the attenuation background is negligible (J. S. Yang, H. Y. Yu, M.Jiang and G. Wang; High order total variation minimization for interiortomography, Inverse Problems, 26(3), Article ID: 035013, 2010, 29pages).

Based on the recent mathematical findings made by Daubechies et al., weadapted a simultaneous algebraic reconstruction technique (SART) forimage reconstruction from a limited number of projections subject to asparsity constraint in terms of an invertible sparsifying transform (H.Y. Yu and G. Wang: SART-type image reconstruction from a limited numberof projections with the sparsity constraint; International Journal ofBiomedical Imaging, Article ID: 934847, 2010, to appear), andconstructed two pseudo-inverse transforms of un-invertible transformsfor the soft-threshold filtering (H. Y. Yu and G. Wang: Soft-thresholdfiltering approach for reconstruction from a limited number ofprojections, Physics in Medicine and Biology, pending revision).

SUMMARY OF THE INVENTION

An exemplary object is to provide a new method and system for providinginterior tomography with sparsity constraints from truncated localprojections, which may or may not be aided by other data and/orknowledge.

Another exemplary object is to provide instant tomography where a ROI or

VOI is imaged without moving an X-ray source on a path around apatient/animal/specimen/object. For purposes of this description the ROIwill be understood to include VOI, and vice versa.

According to one exemplary embodiment, the interior problem can besolved in a theoretically exact and numerically reliable fashion if theROI is subject to an appropriate sparsity image model. Thereconstruction schemes only use projection data associated with linesthrough an ROI or volume of interest (VOI) to be reconstructed, and arereferred to as interior tomography, in contrast with traditional CTreconstruction that does not allow two-side data truncation. Interiortomography enables faithful resolution of features inside an ROI usingdata collected along x-ray beams probing the ROI with knowledge of thesparsity model (e.g., piecewise constant, high-order, and wavelettransform). The reconstruction is to minimize the L1 norm of the ROIimage in the sparsifying transform domain. The sparsifying transform canbe invertible transforms or frames, such as a wavelet transform, Fouriertransform, and other lossless digital image compressive transforms. Andthe sparsity transform can also be un-invertible transform, such as thediscrete gradient transform or discrete difference transform. The L1norm minimization can be, for example, implemented by the steepestdecent method, soft-threshold filtering method and its acceleratedprojected gradient version, as well as pseudo-inverse transforms of thediscrete gradient transform and discrete difference transform.

According to another exemplary embodiment, novel fan-beam or cone-beamtechniques are developed which permit higher temporal resolution at lessradiation dose. That is, CT systems and methods can use interiortomography to provide ultrafast or instantaneous temporal resolution ofa small ROI with or without the need to move an x-ray source on atrajectory around a patient/animal/object, producing a “snapshot”,herein referred to as ultrafast or instant tomography. In addition, theuser can move to another region of interest or “roam” to re-position orenlarge such a snapshot, shifting the CT imaging paradigm. This reducedtime for imaging will enrich image information with improved temporalresolution, and, for example, result in increased numbers of screeningprocedures that can be performed on an individual scanning apparatusproviding additional benefits of reduced requirements for data storageand cost savings. In this type of systems, using some sparsity modelsCS-based interior reconstruction is performed to offer excellent imagingperformance.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing and other objectives, aspects and advantages will bebetter understood from the following detailed description of a preferredembodiment of the invention with reference to the drawings, in which:

FIG. 1 is an example of piecewise constant function on a compactsupport.

FIG. 2 shows the reconstructed images of a modified Shepp-logan phantomafter 60 iterations. (A) is the original phantom, (B) the reconstructionusing the local FBP (after smooth data extrapolation), and (C) thereconstruction using the proposed CS-based interior tomographyalgorithm. The display window is [0.1, 0.4].

FIG. 3 are typical profiles along the white lines in FIG. 2.

FIG. 4 are reconstructed images from a sheep chest CT scan after 60iterations. (A) The image reconstructed using FBP from completeprojections, (B) the reconstruction using local FBP (after smooth dataextrapolation), and (C) the reconstruction using the proposed CS-basedinterior tomography algorithm. The display window is [−800 HU, 700 HU].

FIG. 5 are compressive-sampling-based interior tomography of a mousechest using the CNT-based x-ray source gating technique. (A) The imagereconstructed from a complete global dataset as the gold standard withthe white circle for a cardiac ROI, (B) the local magnification of theROI in (A) and (C) the interior tomography reconstruction from 400projections after 60 iterations (without precise knowledge of asubregion in the ROI).

FIG. 6 are compressive-sampling-based interior tomography of a mousechest using (A) 200, (B) 100 and (C) 50 projections to reduce theradiation dose to 50%, 25% and 12.5% of that for FIG. 5(C),respectively.

FIG. 7 are statistical interior reconstruction results of low contrastShepp-Logan phantom. The left column images are the results of using thetruncated Hilbert transform that are the initial images of theStatistical interior reconstruction, while the right column images arethe results of the Statistical interior reconstruction with TVminimization. The top and bottom rows are respectively the reconstructedimages from Poisson projection data with 200,000 and 50,000 photos

FIG. 8 is the projection model of a discrete image in fan-beam geometry.

FIG. 9 are reconstructed 128×128 images from projection datasets withGaussian noise. The first-row images were reconstructed using our newlydeveloped algorithm, while the second-row counterparts were obtainedwithout the sparsity constraint. The 1st, 2nd, 3rd and 4th columns arereconstructed from 55, 45, 35 and 25 projections, respectively. Thedisplay window is [0 0.5].

FIG. 10 are interior reconstruction results from truncated datasets with(A) 255, (B) 165, (C) 85 and (D) 45 projections, respectively. Thedisplay window is [0 0.5].

FIG. 11 are the implementation frameworks of (A) traditional and (B)soft-threshold filtering methods for total variation minimization.

FIG. 12 are reconstructions from 21 noise-free projections. (A) Thereconstruction using the SART method without the TV minimization, (B)using the steepest descent method for the TV minimization, (C) and (D)using the soft-threshold filtering methods for the TV and TDminimization, respectively. The display window is [0,0.5].

FIG. 13 are reconstructions from 15 noise-free projections. (A)-(D) arecounterparts of that in FIG. 12.

FIG. 14 is the reconstructions from 51 noise-free and overlappedprojections. (A) The original phantom image, (B) the overlappedprojections, and (C) reconstructed result by TD minimization. Thedisplay window for (A) and (C) is [0,0.5].

FIG. 15 is a schematic diagram of an example of hardware on which thepreferred or other embodiments can be implemented.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT

Section I. Alternating-Minimization-Based Interior Tomography

I.1. Theoretical Results

It is well known that the CS theory depends on the principle oftransform sparsity. For an object of interest such as a digital image,we can arrange it as a vector, and in numerous cases there exists anorthonormal basis to make the object sparse in terms of significanttransform coefficients. In CS-based image reconstruction, frequentlyused sparsifying transforms are discrete gradient transforms and wavelettransforms. The discrete gradient sparsifying transform was recentlyutilized in CT reconstruction. This is because the x-ray attenuationcoefficient often varies mildly within organs, and large imagevariations are usually confined to the borders of tissue structures. Asparse gradient image may also be a good image model in industrial orsecurity applications.

Now, let us analyze the possible exactness of interior tomography in theCS framework subject to the TV minimization. Without loss of generality,let us consider a 2D smooth image ƒ({right arrow over (r)})=ƒ(x, y)=ƒ(ρ,θ), ρε[0,1], θε[0,2π) on the compact support unit disk Ω. Its Radontransform can be written as R(s,φ), sε[−1,1], φε[0,π). Suppose that weare only interested in its interior part ƒ(ρ, θ) with ρ<ρ₀, and we onlyknow the corresponding local Radon transforms R(s,φ), |s|<ρ₀, which isalso referred to as local parallel-beam projections. Based on theclassic analysis (see F. Natterer, The mathematics of computerizedtomography. Classics in applied mathematics 2001, Philadelphia: Societyfor Industrial and Applied Mathematics), in general there is no uniquesolution if we only know these local data. For any reconstructed imagefrom the local data set, it can be viewed as an exact reconstructionfrom a complete dataset R(s,φ), sε[−1,1], φε[0, π) and a global dataset{tilde over (R)}(s, φ), ρ₀<|s|≦1, φε[0, π). Although {tilde over (R)}(s,φ)=0 for |s|<ρ₀, it can still produce a non-zero 2D local image g(ρ,θ),ρε[0, ρ₀), θε[0,2π) inside the ROI, which is the reason for thenon-uniqueness.

Lemma I.1: For any Radon transform {tilde over (R)}(s, φ), ρ₀<|s|≦1,φε[0,π), the corresponding reconstructed image g(ρ,θ) with ρ<ρ₀ issmooth and bounded if {tilde over (R)}(s, φ) is continuous and bounded.

Lemma I.2: For the reconstructed image g(ρ,θ) with |ρ|<ρ₀, both

$\frac{\partial_{g}\left( {\rho,\theta} \right)}{\partial\rho}\mspace{14mu} {and}\mspace{14mu} \frac{\partial_{g}\left( {\rho,\theta} \right)}{\rho {\partial\theta}}$

are smooth and bounded.

Theoretically speaking, the L1 norm of the image ƒ(ρ,θ) inside the ROIcan be expressed as:

$\begin{matrix}{{f_{tv} = {\int_{0}^{2\pi}\ {{\theta}{\int_{0}^{\rho_{0}}{{{\mu \left( {\rho,\theta} \right)}}\rho \ {\rho}}}}}},} & \left( {I{.1}} \right)\end{matrix}$

where μ(ρ,θ) represents a sparifying transform. For the commonly usedgradient transform in the medical imaging field, we have

$\begin{matrix}{{{\mu \left( {\rho,\theta} \right)} = \sqrt{\left( \frac{\partial{f\left( {\rho,\theta} \right)}}{\partial\rho} \right)^{2} + \left( \frac{\partial{f\left( {\rho,\theta} \right)}}{\rho {\partial\theta}} \right)^{2}}},} & \left( {I{.2}} \right)\end{matrix}$

which is the gradient magnitude or absolute value of the maximumdirectional derivation at (ρ,θ). If there is no other statement in thispaper, we always assume that μ(ρ,θ) defined by Eq. (I.1) and (I.2)represents the total variation. When there exists an artifact imageg(ρ,θ) due to the data truncation, the total variation will be:

$\begin{matrix}\begin{matrix}{{\overset{\sim}{f}}_{tv} = {\int_{0}^{2\pi}\ {{\theta}{\int_{0}^{\rho_{0}}{{\overset{\sim}{\mu}\left( {\rho,\theta} \right)}\rho \ {\rho}}}}}} \\{= {\int_{0}^{2\pi}\ {{\theta}{\int_{0}^{\rho_{0}}\rho}}}} \\{{{\sqrt{\left( \frac{\partial{f\left( {\rho,\theta} \right)}}{\partial\rho} \right) + {\lambda \left( \frac{\partial_{g}\left( {\rho,\theta} \right)}{\partial\rho} \right)}^{2} + \left( \frac{\partial{f\left( {\rho,\theta} \right)}}{\rho {\partial\theta}} \right) + {\lambda \left( \frac{\partial_{g}\left( {\rho,\theta} \right)}{\rho {\partial\theta}} \right)}^{2}}{\rho}},}}\end{matrix} & \left( {I{.3}} \right)\end{matrix}$

where {tilde over (μ)}(ρ, θ) represents the sparifying transform of areconstructed image including an artifact image g(ρ, θ) and λ is acoefficient. If we can prove that {tilde over (ƒ)}_(tv) can be minimizedat λ=0 for the given ƒ(ρ,θ) and g(ρ, θ), the exactness of interiortomography in the CS framework should hold in this particular case.

Theorem I.1: In the CS framework, it is impossible to reconstructexactly an interior ROI of a general 2D smooth function by minimizingthe total variation Eq. (I.3).

Lemma I.3. Assuming that a circularly symmetric artifact image g(ρ,θ)=g(ρ) is reconstructed from a projection dataset {tilde over (R)}(s,φ)={tilde over (R)}(s) (|s|≦1, φε[0, π)) and {tilde over (R)}(s)≡0 forsε[−a, a]. If g(ρ) is a square integrable function on [0,1] with g(ρ)≡C(ρε[0,a)), then C=0.

Lemma I.4. Assuming that a circularly non-symmetric artifact image g(ρ,θ) is reconstructed from a projection dataset {tilde over (R)}(s, φ)(|s|≦1, φε[0, π)) and {tilde over (R)}(s, φ)≡0 for sε[−a, a]. If g(ρ, θ)is a square integrable function on ρε[0,1] and θε[0,2π] with g(ρ, θ)≡C(ρε[0,a)), then C=0.

Theorem I.2: In the CS framework, an interior ROI of a circularsymmetric piecewise constant function ƒ(ρ) can be exactly determined byminimizing the total variation defined in Eq. (I.3).

Next, let us consider a general piecewise constant function ƒ(ρ, θ)defined on the compact supported unit disk Ω. Without loss ofgenerality, we assume that Ω can be divided into finite sub-regionsΩ_(n), n=1, 2, . . . , N, where each sub-region Ω_(n) has a non-zeroarea measure, on which ƒ(ρ, θ) is a constant. As a result, thesesub-regions also define finitely many boundaries in terms ofarc-segments, each of which is of a non-zero length and differentiablealmost everywhere excluding at most finitely many points. We assume thatan interior ROI Ω₀ (defined by ρ<ρ₀) covers M boundaries and these linesegments are denoted as L₁, L₂, . . . L_(m) . . . , L_(M). While thelength of L_(m) is denoted as t_(m)>0, the difference between the twoneighboring sub-regions is denoted as q_(m). An example is given in FIG.1, where the compact support Ω includes 6 sub-regions and the ROI Ω₀covers 8 arc-segments as boundaries.

Theorem I.3: In the CS framework, an interior ROI of a general compactlysupported function ƒ({right arrow over (r)}) can be exactly determinedby minimizing the total variation defined by Eq. (I.3) if ƒ({right arrowover (r)}) can be decomposed into finitely many constant sub-regions.

All of the above results were proved in our recent publications (see H.Y. Yu, and G. Wang, Compressed sensing based interior tomography. PhysMed Biol, 2009. 54(9): p. 2791-2805; H. Y. Yu, et al., Supplementalanalysis on compressed sensing based interior tomography. Phys Med Biol,2009. 54(18): p. N425-N432; W. M. Han, H. Y. Yu and G. Wang; A generaltotal variation minimization theorem for compressed sensing basedinterior tomography; International Journal of Biomedical Imaging,Article ID: 125871, 2009, 3 pages). In the same spirit of the publishedproofs, by defining a different sparisfying transform μ, our results canbe extended to some interesting families of functions. Particularly, wehave proved that if an ROI/VOI is piecewise polynomial, then the ROI/VOIcan be accurately reconstructed from projection data associated withx-rays through the ROI/VOI by minimizing the high-order total variation(J. S. Yang, H. Y. Yu, M. Jiang and G. Wang; High order total variationminimization for interior tomography, Inverse Problems, 26(3), ArticleID: 035013, 2010, 29 pages).

As an important molecular imaging modality, single-photon emissioncomputed tomography (SPECT) is to reconstruct a radioactive sourcedistribution within a patient or animal. Different from the lineintegral model for x-ray imaging, SPECT projections can bemathematically modeled as an exponentially attenuated Radon transform.In this context, the CT reconstruction may be regarded as a special caseof SPECT (all the attenuation coefficients are zeros). Expanding CTinterior tomography results, we have proved that accurate and stable theinterior SPECT reconstruction of an ROI is feasible from uniformlyattenuated local projection data aided by prior knowledge of asub-region in the ROI. Naturally, the above theoretical results can beextended to SPECT using the same arguments. That is, it is possible toreconstruct a SPECT ROI accurately only from the uniformly attenuatedlocal projections by minimizing the L1 norm of its sparsity transform ifthe distribution function to be reconstructed is piecewiseconstant/polynomial. The same methodology can be employed for magneticresonance imaging.

1.2. OS-SART Based Reconstruction Algorithm

To verify the above theoretical results, we developed a numericalinterior tomography algorithm in the CS framework. The algorithmconsists of two major steps. In the first step, the ordered-subsetsimultaneous algebraic reconstruction technique (OS-SART) (G. Wang andM. Jiang, Ordered-Subset Simultaneous Algebraic ReconstructionTechniques (OS-SART), Journal of X-ray Science and Technology,12:169-77, 2007) was used to reconstruct a digital imageƒ_(m,n)=ƒ(mΔ,nΔ) based on all the truncated local projections, where Δrepresents the sampling interval, m and n are integers. In the secondstep, we minimize the L1 norm for a given sparsifying transform of thediscrete image f_(m,n) using the standard steepest descent method. Thesetwo steps were iteratively performed in an alternating manner.Specifically, the algorithm can be summarized in the followingpseudo-code:

S1. Initialize control parameters α , α_(s) , P_(TV) , and P_(ART) ; S2.Initialize reconstruction k := 0 and f_(m,n) ⁰ = 0 ; S3. Perform OS-SARTreconstruction and TV minimization alternately S3.1 Initialize the loopk := k + 1; f_(m,n) ^(k) := f_(m,n) ^(k−1) ; S3.2 Perform reconstructionfor every projection subset p_(art) to P_(ART) S3.2.1 Perform OS-SARTreconstruction S3.2.1.1 Forward compute the current projections off_(m,n) ^(k) in the p_(art) subset; S.3.2.1.2 Update f_(m,n) ^(k) bybackprojecting the projection errors in the p_(art) subset; S.3.2.2Minimize TV by steepest descent method for p_(tv) = 1 to P_(TV) toS.3.2.2.1 Compute the steepest decent direction d_(m,n) ; S.3.2.2.2Compute normalized coefficient β := max(|f_(m,n) ^(k)|) ÷max(|d_(m,n)|); S.3.2.2.3 Update the reconstructed image f_(m,n) ^(k) =f_(m,n) ^(k) − α × β × d_(m,n) ; S.3.2.2.4 Update the control parameterα = α × α_(s) ; S3.3 Check the stopping criteria

S1 initializes the control parameters α, α_(s), P_(TV) and P_(ART),where α represents the maximal step for the steepest descent to minimizeTV, α_(s) the decreasing scale of α after each computation, P_(TV) thelocal loop time to minimize TV, and P_(ART) denotes the number ofsubsets for OS-SART reconstruction. S2 initializes the reconstructedimage and the main loop count k for alternating iteration procedure S3.S3.2.1.1 computes the forward projections of the current image in thep_(art) subset, where it may include both local truncated and globalscout projections. In our code, we employ a global imaging geometry andadapt a projection mask image to indicate which pixel in the projectiondomain is available. S3.2.1.2 updates ƒ_(m,n) ^(k) by backprojecting theprojection differences, where only the projections in p_(art) subset areused. S3.2.2 define the local loop to minimize the L1 norm. S3.3 decidesif the main loop should be stopped or not.

For the discrete gradient transform, the magnitude of the gradient canbe approximately expressed as:

$\begin{matrix}{\mu_{m,n} \cong {\sqrt{\frac{\begin{matrix}{\left( {f_{{m + 1},n} - f_{m,n}} \right)^{2} + \left( {f_{m,n} - f_{{m - 1},n}} \right)^{2} +} \\{\left( {f_{m,{n + 1}} - f_{m,n}} \right)^{2} + \left( {f_{m,n} - f_{m,{n - 1}}} \right)^{2}}\end{matrix}}{2\nabla^{2}}}.}} & \left( {I{.10}} \right)\end{matrix}$

Correspondingly, the total variation can be defined as

$f_{tv} = {\sum\limits_{m}\; {\sum\limits_{n}\; {\mu_{m,n}.}}}$

Then, we have the steepest descent direction defined by

$\begin{matrix}\begin{matrix}{d_{m,n} = \frac{\partial f_{TV}}{\partial f_{m,n}}} \\{= {\frac{{4f_{m,n}} - f_{{m + 1},n} - f_{{m - 1},n} - f_{m,{n + 1}} - f_{m,{n - 1}}}{\mu_{m,n}} +}} \\{{\frac{f_{m,n} - f_{{m + 1},n}}{\mu_{{m + 1},n}} + \frac{f_{m,n} - f_{{m - 1},n}}{\mu_{{m - 1},n}} + \frac{f_{m,n} - f_{m,{n + 1}}}{\mu_{m,{n + 1}}} +}} \\{{\frac{f_{m,n} - f_{m,{n - 1}}}{\mu_{m,{n - 1}}}.}}\end{matrix} & \left( {I{.11}} \right)\end{matrix}$

For others sparsifying transform, we can deduce the correspondingd_(m,n) easily. To avoid the singularity when computing d_(m,n) usingEq. (I.11), we added a small constant ε to Eq. (I.11) when computing thegradient μ_(m,n). That is,

$\begin{matrix}{\mu_{m,n} \cong {\sqrt{\frac{\begin{matrix}{\left( {f_{{m + 1},n} - f_{m,n}} \right)^{2} + \left( {f_{m,n} - f_{{m - 1},n}} \right)^{2} +} \\{\left( {f_{m,{n + 1}} - f_{m,n}} \right)^{2} + \left( {f_{m,n} - f_{m,{n - 1}}} \right)^{2}}\end{matrix}}{2\nabla^{2}} + ɛ^{2}}.}} & \left( {I{.12}} \right)\end{matrix}$

In our numerical simulation, we assumed a circular scanning locus ofradius 57.0 cm and a fan-beam imaging geometry. We also assumed anequi-spatial virtual detector array of length 12.0 cm. The detector wascentered at the system origin and made always perpendicular to thedirection from the system origin to the x-ray source. The detector arrayincluded 360 elements, each of which is of aperture 0.033 cm. Thisscanning configuration covered a circular FOV of radius 5.967 cm. For acomplete scanning turn, we equi-angularly collected 1300 projections.The reconstructed object was a 2D modified Shepp-logan phantom. Thisphantom is piecewise constant and includes a set of smooth ellipseswhose parameters are listed in Table 1, where a,b represent the x,ysemi-axes, (x₀, y₀) the center of the ellipse, ω denotes the rotationangle, ƒ the relative attenuation coefficient. The units for a,b and(x₀, y₀) are cm. The reconstructed images were in a 256×256 matrixcovering an FOV of radius 10 cm. The 60 iterations took 60 minutes. Forcomparison, we also reconstructed an image using a local FBP method withsmooth extrapolation from the truncated projections into missing data.Some typical reconstructed images were shown in FIG. 2. The typicalprofiles were in FIG. 3. As seen from FIGS. 2 and 3, the reconstructedimages from the proposed algorithm are in a high precision inside theROI.

TABLE 1 Parameters of the 2D modified Shepp-Logan phantom. No. a b x₀ y₀ω f 1 6.900 9.200 0 0 0 1.0 2 6.624 8.740 0 −0.184 0 −0.8 3 1.100 3.1002.200 0 −18. −0.2 4 1.600 4.100 −2.200 0 18.0 −0.2 5 2.100 2.500 0 3.5000 0.1 6 0.460 0.460 0 1.000 0 0.1 7 0.460 0.460 0 −0.100 0 0.1 8 0.4600.230 −0.800 −6.050 0 0.1 9 0.230 0.230 0 −6.060 0 0.1 10 0.230 0.4600.600 −6.060 0 0.1 11 2.000 0.400 5.000 −5.200 60.5 −0.2

To demonstrate the real-world application of the proposed algorithm, weperformed a CT scan of a living sheep, which was approved by VirginiaTech IACUC committee. The chest of a sheep was scanned in fan-beamgeometry on a SIEMENS 64-Slice CT scanner (100 kVp, 150 mAs). The x-raysource trajectory of radius 57.0 cm was used. There were 1160projections uniformly collected over a 360° range, and 672 detectorswere equi-angularly distributed per projection. Thus, the FOV of radius25.05 cm was formed. First, an entire 29.06 cm by 29.06 cm cross-sectionwas reconstructed into 1024×1024 pixels using the popular FBP methodfrom a complete dataset of projections. Second, a trachea was selectedin reference to the reconstructed image. Around the trachea, a circularROI of radius 120 pixels was specified. Then, only the projection datathrough the ROI were kept to simulate an interior scan. Third, the ROIwas reconstructed by the local FBP with smooth data extrapolation andour proposed algorithms, respectively. The results were in FIGS. 4.Comparing the images in FIG. 4, we observe that the proposed algorithmnot only keeps image accuracy but also suppresses image noise.

In vivo mouse imaging experiments were also performed following theprotocols approved by the University of North Carolina at Chapel Hill.Projection images were acquired using a prospective gating approach. ForCT scans carried out in this study, 400 projections were acquired over acircular orbit of 199.5 degrees with a stepping angle of 0.5 degree atsingle frame acquisition. By running the detector at 1 frame/sec (cameraintegration time=500 ms), the scan time was 15-30 min, depending themouse's respiration and heart rates. For the above acquired in vivomouse cardiac projection datasets, we performed a CS-based interiorreconstruction. Using the generalized Feldkamp algorithm (see L. A.Feldkamp, L. C. Davis, and J. W. Kress, Practical cone-beam algorithm.J. Opt. Soc. Am., 1984. 1(A): p. 612-619; G. Wang, et al., A GeneralCone-Beam Reconstruction Algorithm. IEEE Transactions on MedicalImaging, 1993. 12(3): p. 486-496), first we reconstructed a volumetricimage to serve as a global standard for our interior reconstruction.From such an image volume, we specified a circular ROI on a transverseslice to cover the contrast-enhanced beating heart. Then, we created amask image for the ROI and performed a forward projection to generate amask projection. Later the mask projection was binarized to extract theprojection data only through the ROI as our interior scan dataset.Meanwhile, the global projections of 1^(st) and 360^(th) were kept toserve as two scout images. The interior reconstruction was performedusing our CS based algorithm described in section II; the controlparameters were α=0.005, α_(s)=0.997, P_(TV)=2, and P_(ART)=20. Thefinal reconstruction results are in FIG. 5. Because the CS-basediterative reconstruction framework is capable of noise removing, ourCS-based interior reconstruction result has a high SNR than that of theglobal FBP reconstruction.

While the radiation dose to the whole body can be reduced using ourreconstruction method by limiting the x-ray beam to the ROI only, theorgan dose—the dose of the organ which happens to be within the ROIunder interior reconstruction—would remain at the same level. Becausethe CS reconstruction theory is based on the so-called sparsifyingtransform, the radiation dose of local ROI can be further reduced withfewer projection views. However, the smaller the number of projections,the worse the reconstructed image quality (see Chen, G. H., J. Tang, andS. Leng, Prior image constrained compressed sensing (PICCS): A method toaccurately reconstruct dynamic CT images from highly undersampledprojection data sets. Medical Physics, 2008. 35(2): p. 660-663). Tostudy how the projection number affects the reconstructed image quality,200/100/50 projections were uniformly selected from the above mentionedprojections by discarding 1/3/7 projections in every 2/4/8/ projections.For these reduced projections, we can save radiation dose 50%, 75% and87.5%, respectively. During the reconstruction procedure, two globalorthogonal scout projections were also employed. The reconstructedresults were shown in FIG. 6. It can be seen that the image quality wasgood enough even using only 100 projections.

I.3. Statistical Interior Reconstruction Algorithm

In fact, our CS-based interior reconstruction can also be implemented ina statistical framework (Q. Xu, H. Y. Yu, X. Q. Mou and G. Wang, Astatistical reconstruction method for interior problem, Proceedings ofSPIE, to appear in August 2010). We have implemented PWLS, q-GGMRF,SIRTV, and OSSART-TV algorithm. Moreover, we changed the OSSART term ofthe OSSSART-TV algorithm with an OSWLS term considering the statisticalproperty of projection data. Global and truncated projection data whichare both noiseless and noisy (obeying Poisson distribution) were used totest these algorithms.

According to work by Sauer and Bouman (K. Sauer and C. Bouman, A localupdated strategy for iterative reconstruction from projections, IEEETransactions on Signal Processing, 41(2):534-548, 1993), the specificupdate iteration of the quadratic PWLS algorithm can be expressed asfollows:

$\begin{matrix}{{\mu_{j}^{n + 1} = {\mu_{j}^{n} + \frac{{\sum\limits_{i}\; {d_{i}{A_{ij}\left( {y_{i} - {\sum\limits_{k}\; {A_{ik}\mu_{k}^{n}}}} \right)}}} + {\beta {\sum\limits_{k \in N_{j}}\; {w_{jk}\left( {\mu_{k}^{n} - \mu_{j}^{n}} \right)}}}}{{\sum\limits_{i}\; {d_{i}A_{ij}^{2}}} + {\beta {\sum\limits_{k \in N_{j}}\; w_{jk}}}}}},} & \left( {I{.13}} \right)\end{matrix}$

where μ_(j) ^(n) denotes the value of the jth voxel after the nthiteration, d_(i) represent the maximum likelihood estimates of theinverse of the variance of the projection data, β is the penaltyparameter, and N_(j) denotes the neighborhood of the pixel j and w_(jk)are the directional weighting coefficients.

According to the paper by J. B. Thibault et al (J. B. Thibault et al, Athree-dimensional statistical approach to improve image quality formulti-slice helical CT, Medical Physics, 34(11): 4526-4544, 2007), thespecific optimization of the q-GGMRF algorithm is:

$\begin{matrix}{{{\hat{\mu}}_{j}^{n + 1} = {\arg \; \min \left\{ {{\sum\limits_{i}\; {\frac{d_{i}}{2\;}\left( {y_{i} - {\sum\limits_{k}\; {A_{ik}\mu_{k}^{n}}} + {A_{ij}\left( {\mu_{j}^{n} - \mu_{j}} \right)}} \right)^{2}}} + {\beta {\sum\limits_{{\{{j,k}\}} \in C}\; {w_{jk}\frac{{{\mu_{j} - \mu_{k}}}^{p}}{1 + {\frac{\mu_{j} - \mu_{k}}{c}}^{p - q}}}}}} \right\}}},} & \left( {I{.14}} \right)\end{matrix}$

According to the paper by J. Tang et al (J. Tang, et al, Performancecomparison between total variation based compressed sensing andstatistical iterative reconstruction algorithm, Physics in Medicine andBiology, 54(19):5781-5804, 2009), the optimization method of the SIRTValgorithm is similar to the q-GGMRF algorithm. And we have

$\begin{matrix}{{{\hat{\mu}}_{j}^{n + 1} = {\arg \; \min \left\{ {{\sum\limits_{i}\; {\frac{d_{i}}{2\;}\left( {y_{i} - {\sum\limits_{k}\; {A_{ik}\mu_{k}^{n}}} + {A_{ij}\left( {\mu_{j}^{n} - \mu_{j}} \right)}} \right)^{2}}} + {\beta \; {{TV}(\mu)}}} \right\}}},} & \left( {I{.15}} \right)\end{matrix}$

where μ_(j)=μ_(m,n).

Notice that Gauss-Seidel method is adopted in the implementations of theabove three algorithms. Voxels are updated in a random but fixed order.The half-interval search is used to find the root of the derivative.

In our implementation, the measurements are assumed to follow Poissonstatistical model, and the reconstruction process is to maximize theobject function with a prior of total variation (TV) minimization or L1norm minimization. In order to increase the accuracy and stability ofthis algorithm, a small known sub-region in the ROI is needed. The roughresult of the inversion of the truncated Hilbert transform with thesmall known sub-region is regard as the initial image of the statisticalTV iteration. In the numerical simulation experiment, we evaluated theanti-noise property of our algorithm at different dose level (FIG. 7).

Section II. Soft-Thresholding-Based Methods

II.1. Mathematical Principal

Daubechies and her collaborators proposed a general iterativethresholding algorithm to solve linear inverse problems regularized by asparsity constraint and proved its convergence (see I. Daubechies, M.Defrise, and C. De Mol, An iterative thresholding algorithm for linearinverse problems with a sparsity constraint. Communications on Pure andApplied Mathematics, 2004. 57(11): p. 1413-1457; I. Daubechies, M.Fornasier, and I. Loris, Accelerated projected gradient method forlinear inverse problems with sparsity constraints. Journal of FourierAnalysis and Applications, 2008. 14(5-6): p. 764-792). Their approachcan be directly applied for the CT reconstruction from a limited numberof projections. Their major results can be summarized as follows.

Let f=[ƒ₁, ƒ₂, . . . , ƒ_(N)]^(T)ε

^(N) be an object function and g=[g₁, g₂, . . . , g_(M)]^(T)ε

^(M) be a set of measurements. Usually, they are linked by:

g=Af+e,  (II.1)

where A=(a_(mn))ε

^(M)×

^(N) is the linear measurement matrix, and eε

^(M) the measurement noise. Let us define the l_(p) norm of the vector gas

$\begin{matrix}{{g}_{p} = {\left( {\sum\limits_{m = 1}^{M}\; g_{m}^{p}} \right)^{1/p}.}} & \left( {{II}{.2}} \right)\end{matrix}$

In practical applications, we usually omit the subscript p when p=2. Tofind an estimate of f from g, one can minimize the discrepancy Δ(f)

Δ(f)=∥g−Af∥ ².  (II.3)

When the system (II.1) is ill-posed, the solution to Eq. (II.3) is notsatisfactory, and additional constraints are required to regularize thesolution. Particularly, given a complete basis (φ_(γ))_(γεΓ) of thespace

^(N) satisfying

${f = {\sum\limits_{\gamma \in \Gamma}\; {{\langle{f,\phi_{\gamma}}\rangle}\phi_{\gamma}}}},$

and a sequence of strictly positive weights w=(w_(γ))_(γεΓ), we definethe functional Φ_(w, p)(f) by

$\begin{matrix}{{{\Phi_{w,p}(f)} = {{\Delta (f)} + {\sum\limits_{\gamma \in \Gamma}{2\; w_{\gamma}{{\langle{f,\phi_{\gamma}}\rangle}}^{p}}}}},} & \left( {{II}{.4}} \right)\end{matrix}$

where

•,•

represents the inner product and 1≦p≦2. Using the l_(p) norm definition(II.2), let us define the l_(p) norm of a matrix operator A as

$\begin{matrix}{{A}_{p} = {\max\limits_{f \neq 0}{\left( \frac{{{Af}}_{p}}{{f}_{p}} \right).}}} & \left( {{II}{.5}} \right)\end{matrix}$

Let A^(T) be the adjoint operator of A, which is the transpose matrix ofA, the operator A in (II.1) be bounded, and ∥A^(T)A∥<C . In thefollowing, we will assume C=1 because A can always be re-normalized. Tofind an estimate of f from g under the l_(p) norm regularization term

${\sum\limits_{\gamma \in \Gamma}{2\; w_{\gamma}{{\langle{f,\phi_{\gamma}}\rangle}}^{p}}},$

we can minimize Φ_(w, p)(f) defined in (II.4). The minimizer ofΦ_(w, p)(f) can be recursively determined by the soft-thresholdingalgorithm:

f ^(k)=

_(w,p)(f _(k−1) +A ^(T)(g−Af ^(k−1))),  (II.6)

where k=1, 2, . . . is the iteration number, f⁰ the initial value in

^(N), and

$\begin{matrix}{{{_{w,p}(f)} = {\sum\limits_{\gamma \in \Gamma}{{S_{w_{\gamma},p}\left( {\langle{f,\phi_{\gamma}}\rangle} \right)}\phi_{\gamma}}}},} & \left( {{II}{.7}} \right)\end{matrix}$

with S_(w,p)=(F_(w,p))⁻¹ is a one-to-one map from

to its self for p>1 with

F _(w,p)(x)=x+wpsgn(x)|x| ^(p−1).  (II.8)

Particularly,

$\begin{matrix}{{S_{w,{3/2}}(x)} = \left\{ {\begin{matrix}{x - \frac{3\; {w\left( {\sqrt{{9\; w^{2}} + {16{x}}} - {3\; w}} \right)}}{8}} & {{{if}\mspace{14mu} x} \geq 0} \\{x + \frac{3\; {w\left( {\sqrt{{9\; w^{2}} + {16{x}}} - {3\; w}} \right)}}{8}} & {{{if}\mspace{14mu} x} < 0}\end{matrix}.} \right.} & \left( {{II}{.9}} \right)\end{matrix}$

When p=1, we can set

$\begin{matrix}{{S_{w,1}(x)} = \left\{ {\begin{matrix}{x - w} & {{{if}\mspace{14mu} x} \geq w} \\0 & {{{if}\mspace{14mu} {x}} < w} \\{x + w} & {{{if}\mspace{14mu} x} \leq {- w}}\end{matrix}.} \right.} & \left( {{II}{.10}} \right)\end{matrix}$

The main result of Daubechies et al. (I. Daubechies, M. Defrise, and C.De Mol, An iterative thresholding algorithm for linear inverse problemswith a sparsity constraint. Communications on Pure and AppliedMathematics, 2004. 57(11): p. 1413-1457) is that the solution of (II.6)is convergent.

Unfortunately, the convergence speed of Eq. (II.6) is very slow. Tofacilitate practical applications, an accelerated projected gradientmethod was then developed (I. Daubechies, M. Fornasier, and I. Loris,Accelerated projected gradient method for linear inverse problems withsparsity constraints. Journal of Fourier Analysis and Applications,2008. 14(5-6): p. 764-792). When w_(γ)=τ for all γεΓ, Φ_(w, p)(f) can berewritten as

$\begin{matrix}{{\Phi_{w,p}(f)} = {{\Phi_{\tau,p}(f)} = {{\Delta (f)} + {\sum\limits_{\gamma \in \Gamma}{2\tau {{{\langle{f,\phi_{\gamma}}\rangle}}^{p}.}}}}}} & \left( {{II}{.11}} \right)\end{matrix}$

Denote the minimizer of Eq. (II.11) as f* and define

$\begin{matrix}{{{R\left( {f^{*},p} \right)} = \left( {\sum\limits_{\gamma \in \Gamma}{{\langle{f^{*},\phi_{\gamma}}\rangle}}^{p}} \right)^{1/p}},} & \left( {{II}{.12}} \right)\end{matrix}$

which is the l_(p) norm radius of f* in the sparse space, we have theaccelerated projected gradient algorithm

$\begin{matrix}{{f^{k} = {{\mathbb{P}}_{R{({f^{*},p})}}\left( {f^{k - 1} + {\beta^{k}r^{k}}} \right)}},{{{where}\mspace{14mu} r^{k}} = {A^{T}\left( {g - {Af}^{k - 1}} \right)}},{\beta^{k} = \frac{{r^{k}}^{2}}{{{Ar}^{k}}^{2}}},{and}} & \left( {{II}{.13}} \right)\end{matrix}$

$\begin{matrix}{{{{\mathbb{P}}_{R{({f^{*},p})}}(f)} = {{_{\mu,p}(f)} = {\sum\limits_{\gamma \in \Gamma}{{S_{\mu,p}\left( {\langle{f,\phi_{\gamma}}\rangle} \right)}\phi_{\gamma}}}}},} & \left( {{II}{.14}} \right)\end{matrix}$

with an adapted soft-threshold μ=μ(R(f*, p),f) depending on R(f*, p) andf. When R(f,p)≦R(f*, p), μ(R(f*, p),f)=0 and

_(R(f*,p))(f)=f. When R(f, p)>R(f*, p), the adapted threshold μ shouldbe chosen to satisfy

R

_(R(f*,p))(f),p)=R

_(μ,p)(f),p)=R(f*,p).  (II.15)

Regarding the algorithm (II.13), we have several comments in order.

First, although Daubechies et al. only proved the convergence for thecase p=1, we believe that it should stand for 1≦p≦2. Second, while wehave previously assumed that ∥A^(T)A|<C and C=1, it can be proved thatthe algorithm (II.13) holds for any positive C. Third, it is generallyimpossible to know the exact value of R(f*, p) but we can have anapproximate estimate.

II.2. SART-Type Algorithm

In the context of image reconstruction, each component of the function fin Eq. (II.1) is interpreted as an image pixel with N being the totalpixel number. Each component of the function g is a measured datum withM being the product of the number of projections and the number ofdetector elements. In fan-beam geometry with a discrete image grid, then^(th) pixel can be viewed as a rectangular region with a constant valueƒ_(n), the m^(th) measured datum g_(m) as an integral of areas of pixelspartially covered by a narrow beam from an x-ray source to a detectorelement and respectively weighted by the corresponding x-ray linearattenuation coefficients. Thus, the component a_(mn) in Eq. (II.1) canbe understood as the interaction area between the n^(th) pixel and them^(th) fan-beam path (FIG. 8). While the whole matrix A represents theforward projection, A^(T) implements the back projection. The SART-typesolution to Eq. (II.1) can be written as:

$\begin{matrix}{{f_{n}^{k} = {f_{n}^{k - 1} + {\lambda^{k}\frac{1}{a_{+ n}}{\sum\limits_{m = 1}^{M}\; {\frac{a_{mn}}{a_{m +}}\left( {g_{m} - {A_{m}f^{k - 1}}} \right)}}}}},} & \left( {{II}{.16}} \right)\end{matrix}$

where

${a_{+ n} = {{\sum\limits_{m = 1}^{M}a_{mn}} > 0}},{a_{m +} = {{\sum\limits_{n = 1}^{N}\; a_{mn}} > 0}},$

A_(m) is the m^(th) row of A, k the iteration index, and 0<λ^(k)<2 afree relaxation parameter. Let Λ^(+N)ε

^(N)×

^(N) be a diagonal matrix with

$\Lambda_{nn}^{+ N} = \frac{1}{a_{+ n}}$

and Λ^(M+)ε

^(M)×

^(M) be a diagonal matrix with

$\begin{matrix}{{\Lambda_{m\; m}^{M +} = \frac{1}{a_{m +}}},} & {{Eq}.\mspace{14mu} \left( {{II}{.16}} \right)}\end{matrix}$

can be rewritten as:

f ^(k) =f ^(k−1)+λ^(k) {tilde over (r)} ^(k),  (II.17)

with

{tilde over (r)} ^(k)=Λ^(+N) A ^(T)Λ^(M+)(g−Af ^(k−1)).  (II.18)

Due to the introduction of Λ^(+N) and Λ^(M+), Eq. (II.18) cannot bedirectly applied to solve Eq. (II.13). However, we can modify Eq.(II.18) to obtain a new r^(k) defined as

$\begin{matrix}{r^{k} = {{\frac{A^{T}}{{\Lambda^{+ N}A^{T}\Lambda^{M +}}}{\overset{\sim}{r}}^{k}} = {\alpha {{\overset{\sim}{r}}^{k}.}}}} & \left( {{II}{.19}} \right)\end{matrix}$

Substituting Eq. (II.19) into Eq. (II.13), we have a SART-type algorithm

f ^(k)

_(R(f*,p))(f ^(k−1)+αβ^(k) {tilde over (r)} ^(h)),  (II.20)

with

$\beta^{k} = {\frac{{\overset{\sim}{r}}^{k}}{{{A{\overset{\sim}{r}}^{k}}}^{2}}.}$

The heuristic rationale for the above modification is to incorporate theSART-type weighting scheme for a more uniform convergence behavior. αcan be written as

$\begin{matrix}{{\alpha^{2} = {\frac{\max\limits_{1 \leq n \leq N}\left( {A^{T}{AI}} \right)}{\max \left( {{\Lambda^{+ N}A^{T}\Lambda^{M +}\Lambda^{M +}A\; \Lambda^{+ N}I}} \right)}\alpha_{0}^{2}}}{with}} & \left( {{II}{.21}} \right) \\{\alpha_{0}^{2} = {\frac{\max\limits_{1 \leq n \leq N}\left( {\Lambda^{+ N}A^{T}\Lambda^{M +}\Lambda^{M +}A\; \Lambda^{+ N}I} \right)}{{{\Lambda^{+ N}A^{T}\Lambda^{M +}}} \cdot {{\Lambda^{M +}A\; \Lambda^{+ N}}}}{\frac{{A^{T}} \cdot {A}}{\max\limits_{1 \leq n \leq N}\left( {A^{T}{AI}} \right)}.}}} & \left( {{II}{.22}} \right)\end{matrix}$

In practical applications, we can set α₀ to a reasonably large constantsuch as 2.0 in our simulation in the next section. If the algorithm doesnot converge, we can reduce α₀ until the algorithm converges.

For a basis (φ_(γ))_(γεΓ) of the space

^(N), in which f has a sparse representation. Our SART-type CT algorithmregularized by sparsity can be summarized in the following pseudo-code:

S1. Initialize α₀, f⁽⁰⁾ and k; S2. Estimate R(f*, p); S3. Pre-compute α,a_(+n) and a_(m+); S4. Update the current estimation iteratively: S4.1.k := k + 1; S4.2. {tilde over (r)}_(k) := Λ^(+N)A^(T)Λ^(M+)(g −Af^(k−1));${{{S4}{{.3}.\; \beta^{k}}}:=\frac{{{\overset{\sim}{r}}^{k}}^{2}}{{{A{\overset{\sim}{r}}^{k}}}^{2}}};$S4.4. {tilde over (f)}^(k) := f^(k−1) + αβ^(k){tilde over (r)}^(k);S.4.5. Compute the sparse transform φ_(γ) :=  

{tilde over (f)}^(k), φ_(γ )

 for γ ε Γ; S.4.6. Estimate the adapted threshold μ; S.4.7. Perform thesoft-thresholding {tilde over (φ)}_(γ) := S_(μ,p)(φ_(γ));${{S{.4}{{.8}.\; {Perform}}\mspace{14mu} {the}\mspace{14mu} {inverse}\mspace{14mu} {sparse}\mspace{14mu} {transform}\mspace{14mu} f^{k}}:={\sum\limits_{\gamma \in \Gamma}{{\overset{\sim}{\varphi}}_{\gamma}\phi_{\gamma}}}};$S.5. Go to S.4 until certain convergence criteria are satisfied.

In the above pseudo-code, S.4.5 represents a sparse transform in a basis(φ_(γ))_(γεΓ). It can be either orthonormal (e.g. Fourier transform) ornon-orthonormal, and φ_(γ) is the corresponding coefficient in thesparse space. In S.4.6, the adapted threshold μ can be estimated by adichotomy searching method. S.4.7 performs the inverse sparse transform.Finally, the stopping criteria for S.5 can be either the maximumiteration number is reached or the relative reconstruction error (RRE)comes under a pre-defined threshold:

$\begin{matrix}{E^{k} = {\frac{{f^{k} - f^{*}}}{f^{*}} \times 100.}} & \left( {{II}{.23}} \right)\end{matrix}$

The above-proposed algorithm was implemented in MatLab subject to asparsity constraint in terms of an invertible wavelet transform. Thealgorithm was implemented with an exemplary Haar wavelet transform andtested with a modified Shepp-Logan phantom. In our simulation, a 128×128phantom image was in a compact support of radius 10 cm. An equi-spatialdetector array was of length 20 cm. The array was centered at the systemorigin and made perpendicular to the direction from the origin to thex-ray source. The array consisted of 128 elements. While the number ofphantom image pixels was 16384, there were only 1708 non-zero waveletcoefficients. For each of selected numbers of projections over afull-scan, the corresponding projection dataset was acquired based on adiscrete projection model and a Gaussian noise model. The reconstructedimages are in FIG. 9. Then, this approach was applied for interiortomography with a detector array of length 10 cm to produce thecorresponding results in FIG. 10.

II.3. Pseudo-Inverses of DGT and DDT

The discrete gradient transform (DGT) has been widely utilized as asparsifying operator in CS-inspired CT reconstruction. This kind ofalgorithms can be divided into two major steps (FIG. 11 (a)). In thefirst step, an iteration formula (e.g. SART) is used to update areconstructed image for data discrepancy reduction. In the second step,a search method (e.g. the standard steepest descent technique) is usedin an iterative framework for TV minimization. These two steps need tobe iteratively performed in an alternating manner. However, there are nostandard stopping and parameter selection criteria for the second step.Usually, these practical issues are addressed in an Ad hoc fashion. Onthe other hand, soft-threshold nonlinear filtering was proved to be aconvergent and efficient algorithm for the l₁ norm minimizationregularized by a sparsity constraint. Unfortunately, because the DGT isnot invertible, it does not satisfy the restricted isometry property(RIP) required by the CS theory and soft-threshold algorithm. In otherwords, the soft-threshold algorithm cannot be directly applied for TVminimization. Motivated by this challenge, here we construct twopseudo-inverse transforms and apply the soft-threshold filtering forimage reconstruction from a limited number of projections.

Let us assume that a digital image satisfies the so-called Neumannconditions on the boundary:

ƒ_(0,j)ƒ_(1,j) and ƒ_(I,j)=f_(I+1,j) for 1≦j≦J,

ƒ_(i,0)=ƒ_(i,1) and ƒ_(i,J)=f_(i,J+1) for 1≦i≦I.  (II.24)

Then, the standard isotropic discretization of TV can be expressed as

$\begin{matrix}{{{{TV}(f)} = {\sum\limits_{i = 1}^{I}{\sum\limits_{j = 1}^{J}d_{i,j}}}},{d_{i,j} = {\sqrt{\left( {f_{i,j} - f_{{i + 1},j}} \right)^{2} + \left( {f_{i,j} - f_{i,{j + 1}}} \right)^{2}}.}}} & \left( {{II}{.25}} \right)\end{matrix}$

We re-write Eq. (II.4) for CT reconstruction problem under theconstraint of sparse gradient transform as

Φ_(w,1)(f)=Δ(f)+2wTV(f).  (II.26)

Note that there does not exist a frame such that d_(i,j)=

f, Φ_(i, j)

, the solution Eq. (II.6) can not be directly applied to minimizeΦ_(w,1)(f) defined by Eq. (II.25). However, we can construct apseudo-inverse of the DGT as follows. Assume that

$\begin{matrix}{{{\overset{\sim}{f}}_{n}^{k} = {f_{n}^{k - 1} + {\lambda^{k}\frac{1}{a_{+ n}}{\sum\limits_{m = 1}^{M}{\frac{a_{m,n}}{a_{m +}}\left( {g_{m} - {A_{m}f^{k - 1}}} \right)}}}}},} & \left( {{II}{.27}} \right)\end{matrix}$

is the update from the projection constraint in the current iterationstep k, which is exactly the same as Eq. (II.16). We can compute

d _(i,j) ^(k)=√{square root over (({tilde over (ƒ)}_(i,j) ^(k)−{tildeover (ƒ)}_(i+1,j) ^(k))²+({tilde over (ƒ)}_(i,j) ^(k)−{tilde over(ƒ)}_(i,j+1) ^(k))²)}.  (II.28)

According to the soft-threshold operation in Eq. (II.10), when d_(i,j)^(k)<w we can adjust the value of {tilde over (ƒ)}_(i,j) ^(k), {tildeover (ƒ)}_(i+1,j) ^(k) and {tilde over (ƒ)}_(i,j+1) ^(k) to make d_(i,j)^(k)=0, and when d_(i,j) ^(k)≧w we can reduce the values of ({tilde over(ƒ)}_(i,j) ^(k)−{tilde over (ƒ)}_(i+1,j) ^(k))² and ({tilde over(ƒ)}_(i,j) ^(k)−{tilde over (ƒ)}_(i,j+1) ^(k))² to perform thefiltering. That is, we can construct the following pseudo-inverse:

$\begin{matrix}{{f_{i,j}^{k} = {\frac{1}{4}\left( {{2f_{ij}^{k,a}} + f_{i,j}^{k,b} + f_{i,j}^{k,c}} \right)}},} & \left( {{II}{.29}} \right) \\{f_{i,j}^{k,a} = \left\{ \begin{matrix}{\frac{{2{\overset{\sim}{f}}_{i,j}^{k}} + {\overset{\sim}{f}}_{{i + 1},j}^{k} + {\overset{\sim}{f}}_{i,{j + 1}}^{k}}{4},} & {{{if}\mspace{14mu} d_{i,j}^{k}} < w} \\{{{\overset{\sim}{f}}_{i,j}^{k} - \frac{w\left( {{2{\overset{\sim}{f}}_{i,j}^{k}} - {\overset{\sim}{f}}_{{i + 1},j}^{k} - {\overset{\sim}{f}}_{i,{j + 1}}^{k}} \right)}{4d_{i,j}^{k}}},} & {{{{if}\mspace{14mu} d_{i,j}^{k}} \geq w},}\end{matrix} \right.} & \left( {{II}{.30}} \right) \\{f_{i,j}^{k,b} = \left\{ \begin{matrix}{\frac{{\overset{\sim}{f}}_{i,j}^{k} + {\overset{\sim}{f}}_{{i - 1},j}^{k}}{2},} & {{{if}\mspace{14mu} d_{{i - 1},j}^{k}} < w} \\{{{\overset{\sim}{f}}_{i,j}^{k} - \frac{w\left( {{\overset{\sim}{f}}_{i,j}^{k} - {\overset{\sim}{f}}_{{i - 1},j}^{k}} \right)}{2d_{{i - 1},j}^{k}}},} & {{{{if}\mspace{14mu} d_{{i - 1},j}^{k}} \geq w},}\end{matrix} \right.} & \left( {{II}{.31}} \right) \\{f_{i,j}^{k,c} = \left\{ \begin{matrix}{\frac{{\overset{\sim}{f}}_{i,j}^{k} + {\overset{\sim}{f}}_{i,{j - 1}}^{k}}{2},} & {{{if}\mspace{14mu} d_{i,{j - 1}}^{k}} < w} \\{{{\overset{\sim}{f}}_{i,j}^{k} - \frac{w\left( {{\overset{\sim}{f}}_{i,j}^{k} - {\overset{\sim}{f}}_{i,{j - 1}}^{k}} \right)}{2d_{i,{j - 1}}^{k}}},} & {{{if}\mspace{14mu} d_{i,{j - 1}}^{k}} \geq {w.}}\end{matrix} \right.} & \left( {{II}{.32}} \right)\end{matrix}$

In summary, we have a soft-threshold algorithm for the TV minimizationin the following pseudo-code (FIG. 11( b)):

S1: Initialize the parameters k , w ; S2: Update the currentreconstruction using Eq.(II.16); S3: Perform the non-linear filter usingEq.(II.29); S4: Go to S2 until the stopping criterion is met.

In addition to the DGT, there are other possible sparse transforms. Forexample, we can define a total difference (TD) of f as

$\begin{matrix}{{{{TD}(f)} = {\sum\limits_{i = 1}^{I}{\sum\limits_{j = 1}^{J}d_{i,j}}}},{d_{i,j} = {{{f_{i,j} - f_{{i + 1},j}}} + {{f_{i,j} - f_{i,{j + 1}}}}}},} & \left( {{II}{.33}} \right)\end{matrix}$

and rewrite Eq. (II.4) as

Φ_(w,1)(f)=Δ(f)+2wTD(f).  (II.34)

We call d_(i,j) in Eq. (II.33) a discrete difference transform (DDT).Similar to what we have done for DGT, after the soft-thresholdfiltering, we can construct a pseudo-inverse of ƒ_(i,j) ^(k) as

$\begin{matrix}{{f_{i,j}^{k} = {\frac{1}{4}\left( {{q\left( {w,{\overset{\sim}{f}}_{i,j}^{k},{\overset{\sim}{f}}_{{i + 1},j}^{k}} \right)} + {q\left( {w,{\overset{\sim}{f}}_{i,j}^{k},{\overset{\sim}{f}}_{i,{j + 1}}^{k}} \right)} + {q\left( {w,{\overset{\sim}{f}}_{i,j}^{k},{\overset{\sim}{f}}_{i,{j - 1}}^{k}} \right)} + {q\left( {w,{\overset{\sim}{f}}_{i,j}^{k},{\overset{\sim}{f}}_{{i - 1},j}^{k}} \right)}} \right)}},} & \left( {{II}{.35}} \right) \\{\mspace{79mu} {{q\left( {w,y,z} \right)} = \left\{ \begin{matrix}{\frac{y + z}{2},} & {{{if}\mspace{14mu} {{y - z}}} < w} \\{{y - \frac{w}{2}},} & {{{if}\mspace{14mu} \left( {y - z} \right)} \geq w} \\{{y + \frac{w}{2}},} & {{{if}\mspace{14mu} \left( {y - z} \right)} \leq {- {w.}}}\end{matrix} \right.}} & \left( {{II}{.36}} \right)\end{matrix}$

That is, we have a soft-threshold algorithm for TD minimization in thefollowing pseudo-code:

S1: Initialize the parameters k , w ; S2: Update the currentreconstruction using Eq.(II.16); S3: Perform the non-linear filter usingEq.(II.36); S4: Go to S2 until the stopping criterion is met.

To facilitate practical applications, an accelerated projected gradientmethod can directly develop to automatically select the parameters w forthe above soft-threshold filtering operations to minimize the TD and TV.And all of those results can be directly used for interiorreconstruction.

To demonstrate the validity of the proposed algorithms, we implementedthem in MatLab and performed numerical tests. We assumed a circularscanning locus of radius 57.0 cm and fan-beam geometry. The object was amodified Shepp-Logan phantom in a compact support with a radius of 10.0cm. We used an equi-distance virtual detector array of length 20.0 cm.The detector was centered at the system origin and made perpendicular tothe direction from the origin to the x-ray source. The detector arrayconsisted of 256 elements. For each of our selected numbers ofprojections over a full-scan range, we first equi-angularly acquired thecorresponding projection dataset based on the discrete projection modelshown in FIG. 8. Then, we reconstructed the images using the followingfour methods: (1) the classical SART iteration method without theregularization of sparsity, (2) the TV minimization algorithm using thesteepest descent search method, (3) the TV minimization algorithm withsoft-threshold filtering, and (4) the TD minimization method withsoft-threshold filtering. For all the above methods, the stoppingcriterion was defined as reaching the maximum iteration number 2,000.The threshold w for the third and fourth algorithms was set 0.004. FIGS.12 and 13 show the reconstructed 256×256 images from 21 and 15projections, respectively. In general, the proposed algorithms performmuch better than the available steepest descent algorithm.

II.4. Interior Reconstruction from Coded-Aperture-Generated Projections

In recent years, coded aperture is a new technology to improve imagequality and reduce radiation dose. Using the multi-source and codedaperture technology, we can obtain a compressive sampling based imagingsystem with an optimized sampling pattern allowing flexibility ofsource-multiplexing, projection modulation and overlapping. Toreconstruct an image from aperture coded projections, we must solve thereconstruction problem from coded projections. There are numerous casesin this category.

For example, we developed a POCS-based algorithm to solve the so-calledoverlapping problem (G. Wang, L. Yang, Y. Lu: Method for ImageReconstruction from Overlapped Projections, VTIP No.: 10-058, Dec. 2,2009; L. Yang, Y. Lu, and G. Wang, Compressed sensing based imagereconstruction from overlapped projections, International Journal ofBiomedical Imaging, to appear).

As another example, we developed a SART-based formula for this problem.Let f=[ƒ₁, ƒ₂, . . . , ƒ_(N)]^(T)ε

^(N) be an object function and g=[g₁, g₂, . . . , g_(M)]^(T)ε

^(M) be a set of measurements. Usually, for the two-source case, theyare linked by:

g=e ^(−A) ¹ ^(f) +e ^(−A) ² ^(f),  (II.37)

where A₁=(a_(mn))ε

^(M)×

^(N) and A₂=(a_(mn))ε

^(M)×

^(N) are the linear measurement matrix. Assume that the currentestimation of the image is f^(k), we have an error image Δf^(k)=f−f^(k)and hence

$\begin{matrix}\begin{matrix}{g = {^{- {A_{1}{({f^{k} + {\Delta \; f^{k}}})}}} + ^{- {A_{2}{({f^{k} + {\Delta \; f^{k}}})}}}}} \\{= {^{{- A_{1}}f^{k}^{{- A_{1}}\Delta \; f^{k}}} + {^{{- A_{2}}f^{k}}^{{- A_{2}}\Delta \; f^{k}}}}} \\{{\approx {{^{{- A_{1}}f^{k}}\left( {1 - {A_{1}\Delta \; f^{k}}} \right)} + {^{{- A_{2}}f^{k}}\left( {1 - {A_{2}\Delta \; f^{k}}} \right)}}},}\end{matrix} & \left( {{II}{.38}} \right)\end{matrix}$

where the 1^(st) order Taylor expansion has been used. Eq. (II.38)implied that

e ^(−A) ¹ ^(f) ^(k) +e ^(−A) ² ^(f) ^(f) −g=(e ^(−A) ¹ ^(f) ^(k) A ₁ +e^(−A) ² ^(f) ^(k) A ₂)Δf ^(k).  (II.39)

Consider (e^(−A) ¹ ^(f) ^(k) +e^(−A) ² ^(f) ^(k) −g) as the measurementdata and (e^(−A) ¹ ^(f) ^(k) A₁+e^(−A) ² ^(f) ^(k) A₂) as themeasurement matrix, we can use our SART-type algorithm (Eq. II.16) tosolve Δf^(k). However, it is not necessary to obtain an exact solutionfor Δf^(k). Instead, we only need one step iteration to estimate Δf^(k),then update the current image by f^(k+1)=f^(k)+Δ{tilde over (f)}^(k).This algorithm is convergent and stable. And it can be used for bothglobal and interior reconstruction. The algorithm for the overlappedprojections can be summarized as:

S1. Initialize α, f⁽⁰⁾ and k; S2. Estimate R(f*, p); S3. Update thecurrent estimation iteratively: S3.1. k := k + 1; S3.2. Compute A =e^(−A) ¹ ^(f) ^(k) A₁ + e^(−A) ² ^(f) ^(k) A₂ and {tilde over (g)} = e^(−A) ¹ ^(f) ^(k) + e^(−A) ² ^(f) ^(k) − g; S3.3. Compute a_(+n) anda_(m+); S3.4. Estimate Δf^(k) by the formula Δ{tilde over (f)}^(k) :=Λ^(+N)A^(T)Λ^(M+){tilde over (g)}; S3.5. {tilde over (f)}^(k) :=f^(k−1) + αΔ{tilde over (f)}^(k); S.3.6. Compute the sparse transformφ_(γ) :=  

{tilde over (f)}^(k),φ_(γ )

 for γ ε Γ; S.3.7. Estimate the adapted threshold μ; S.3.8. Perform thesoft-thresholding {tilde over (φ)}_(γ) := S_(μ,p)(φ_(γ));${{S{.3}{{.9}.\; {Perform}}\mspace{14mu} {the}\mspace{14mu} {inverse}\mspace{14mu} {sparse}\mspace{14mu} {transform}\mspace{14mu} f^{k}}:={\sum\limits_{\gamma \in \Gamma}{{\overset{\sim}{\varphi}}_{\gamma}\phi_{\gamma}}}};$S.4. Go to S.3 until certain the convergence criteria are satisfied.

The above algorithm was implemented in Matlab and tested by a numericalphantom. In our numerical simulation, we assumed two x-ray sources andone shared equi-distant detector. Totally, 51 projections were acquiredin a full scan range, and the TD sparsity was employed. As shown in FIG.14, our results are excellent.

FIG. 15 is a schematic diagram of an example of hardware 100 on whichthe preferred or other embodiments can be implemented. The imagingcomponent 102 can be any imaging component capable of operating asdescribed above, any equivalent thereof, or any device for receivingimaging data remotely or from storage. The processing component 104 canbe any processor capable of performing the operations disclosed above orany equivalents thereof. The output 106 can include one or more of adisplay, persistent storage, a printer, a communication facility fortransmitting the results remotely, or any other form of output. Thesoftware for performing the operations disclosed above can be suppliedon any form of persistent storage 108 or in any other manner, e.g., overa network connection.

While a preferred embodiment has been set forth above, those skilled inthe art who have reviewed the present disclosure will readily appreciatethat other embodiments can be realized within the scope of theinvention. For example, numerical values are illustrative rather thanlimiting, as are recitations of particular software. Therefore, thepresent invention should be construed as limited only by the appendedclaims.

What is claimed is:
 1. A method for compressed sensing based interiortomography, the method comprising: (a) obtaining a local projectiondataset that directly involves a region-of-interest (ROI) orvolume-of-interest (VOI); (b) reconstructing, in a computing device, theROI/VOI by minimizing an appropriate combination of (i) an appropriateimage norm of a sparsifying transform of the underlying image to bereconstructed , (ii) the data discrepancy in the projection domain (orin the Hilbert transform domain or equivalent), and (iii) other possibleconstraints such as bounds of image intensities; and (c) outputting thereconstructed ROI/VOI.
 2. The method of claim 1, wherein the image normis an L0 norm or Lp norm with 1≦p≦2, or a mixed norm.
 3. The method ofclaim 1, wherein the dataset is measured using an x-ray imaging systemor in another straight or approximately straight ray imaging geometry.4. The method of claim 1, wherein the projection dataset is measured interms of functions of weighted sums of unknowns along a bundle of pathsthat are close to each other.
 5. The method of claim 3, wherein theimaging system uses a single or multi-sources.
 6. The method of claim 5,wherein multi-sources are modulated by coded apertures, share one ormore detectors, and are fired simultaneously or sequentially.
 7. Themethod of claim 1, wherein the minimization is with respect to an L0norm or Lp norm with 1≦p≦2, or a mixed norm.
 8. The method of claim 1,wherein the said sparsifying transform is an invertible transform orframe.
 9. The method of claim 8, wherein the invertible transform orframe is a wavelet transform, Fourier transform, or another losslessimage compressive transform or encoding method.
 10. The method of claim1, wherein the said sparsifying transform is an un-invertible transform.11. The method of claim 10, wherein the un-invertible transform is adiscrete gradient transform, discrete difference transform, orhigh-order transform.
 12. The method of claim 1, wherein step (a)includes global projection measurements in terms of two or more scoutviews.
 13. The method of claim 1, wherein step (b) comprises: (i)minimizing a data discrepancy; and (ii) minimizing the image norm;wherein steps (i) and (ii) are iteratively performed in an alternatingmanner.
 14. The method of claim 13, wherein step (i) is implemented inan OS-SART framework.
 15. The method of claim 13, wherein step (i) isimplemented in a statistical framework such as using a maximumlikelihood (ML) transmission CT approach.
 16. The method of claim 15,wherein the statistical framework is a maximum-likelihood (ML)reconstruction with a signal sparsity model based constraint or penalty.17. The method of claim 16, wherein the signal sparsity model basedconstraint is a total variation.
 18. The method of claim 13, where instep (ii) is implemented using a steepest decent search method.
 19. Themethod of claim 13, wherein step (ii) is implemented by a soft-thresholdfiltering method.
 20. The method of claim 19, wherein the soft thresholdfiltering method comprises: (i) computing a sparse transform; (ii)estimating an adapted threshold; (iii) performing soft-thresholdfiltering; and (iv) performing an inverse sparse transform.
 21. Themethod of claim 20, wherein the inverse transform of a discrete gradienttransform is constructed as $\begin{matrix}{{f_{i,j}^{k} = {{\lambda_{a}f_{i,j}^{k,a}} + {\lambda_{b}f_{i,j}^{k,b}} + {\lambda_{c}f_{i,j}^{k,c}}}},} \\{f_{i,j}^{k,a} = \left\{ \begin{matrix}{\frac{{2{\overset{\sim}{f}}_{i,j}^{k}} + {\overset{\sim}{f}}_{{i + 1},j}^{k} + {\overset{\sim}{f}}_{i,{j + 1}}^{k}}{4},} & {{{if}\mspace{14mu} d_{i,j}^{k}} < w} \\{{{\overset{\sim}{f}}_{i,j}^{k} - \frac{w\left( {{2{\overset{\sim}{f}}_{i,j}^{k}} - {\overset{\sim}{f}}_{{i + 1},j}^{k} - {\overset{\sim}{f}}_{i,{j + 1}}^{k}} \right)}{4d_{i,j}^{k}}},} & {{{{if}\mspace{14mu} d_{i,j}^{k}} \geq w},}\end{matrix} \right.} \\{f_{i,j}^{k,b} = \left\{ \begin{matrix}{\frac{{\overset{\sim}{f}}_{i,j}^{k} + {\overset{\sim}{f}}_{{i - 1},j}^{k}}{2},} & {{{if}\mspace{14mu} d_{{i - 1},j}^{k}} < w} \\{{{\overset{\sim}{f}}_{i,j}^{k} - \frac{w\left( {{\overset{\sim}{f}}_{i,j}^{k} - {\overset{\sim}{f}}_{{i - 1},j}^{k}} \right)}{2d_{{i - 1},j}^{k}}},} & {{{{if}\mspace{14mu} d_{{i - 1},j}^{k}} \geq w},}\end{matrix} \right.} \\{f_{i,j}^{k,c} = \left\{ \begin{matrix}{\frac{{\overset{\sim}{f}}_{i,j}^{k} + {\overset{\sim}{f}}_{i,{j - 1}}^{k}}{2},} & {{{if}\mspace{14mu} d_{i,{j - 1}}^{k}} < w} \\{{{\overset{\sim}{f}}_{i,j}^{k} - \frac{w\left( {{\overset{\sim}{f}}_{i,j}^{k} - {\overset{\sim}{f}}_{i,{j - 1}}^{k}} \right)}{2d_{i,{j - 1}}^{k}}},} & {{{{if}\mspace{14mu} d_{i,{j - 1}}^{k}} \geq w};}\end{matrix} \right.}\end{matrix}$ where λ_(a)+λ_(b)+λ_(c)=1.
 22. The method of claim 20,wherein the inverse transform of a discrete gradient transform is anynon-linear filter for the reconstructed image to achieve the goal ofsoft-threshold filtering with respect to a given threshold.
 23. Themethod of claim 20, wherein the inverse transform of a discretedifference transform is constructed as $\begin{matrix}{{f_{i,j}^{k} = {{\lambda_{a}{q\left( {w,{\overset{\sim}{f}}_{i,j}^{k},{\overset{\sim}{f}}_{{i + 1},j}^{k}} \right)}} + {\lambda_{b}{q\left( {w,{\overset{\sim}{f}}_{i,j}^{k},{\overset{\sim}{f}}_{i,{j + 1}}^{k}} \right)}} + {\lambda_{c}{q\left( {w,{\overset{\sim}{f}}_{i,j}^{k},{\overset{\sim}{f}}_{i,{j - 1}}^{k}} \right)}} + {\lambda_{d}{q\left( {w,{\overset{\sim}{f}}_{i,j}^{k},{\overset{\sim}{f}}_{{i - 1},j}^{k}} \right)}}}},} \\{\mspace{79mu} {{q\left( {w,y,z} \right)} = \left\{ \begin{matrix}{\frac{y + z}{2},} & {{{if}\mspace{14mu} {{y - z}}} < w} \\{{y - \frac{w}{2}},} & {{{if}\mspace{14mu} \left( {y - z} \right)} \geq w} \\{{y + \frac{w}{2}},} & {{{{if}\mspace{14mu} \left( {y - z} \right)} \leq {- w}};}\end{matrix} \right.}}\end{matrix}$ where λ_(a)+λ_(b)+λ_(c)+λ_(d)=1.
 24. The method of claim20, wherein the inverse transform of a discrete difference transform isa non-linear filter for the reconstructed image to achieve the goal ofsoft-threshold filtering with respect to a given threshold.
 25. Themethod of claim 20, wherein the inverse transform of a un-invertiblesparsifying transform is constructed as a non-linear filter for thereconstructed image to achieve the goal of soft-threshold filtering withrespect to a given threshold.
 26. A system for compressed sensing basedinterior tomography, the system comprising: (a) an imaging component forobtaining a local projection dataset that directly involves aregion-of-interest (ROI) or volume-of-interest (VOI); (b)reconstructing, in a computing device, the ROI/VOI by minimizing anappropriate combination of (i) an appropriate image norm of asparsifying transform of the underlying image to be reconstructed , (ii)the data discrepancy in the projection domain (or in the Hilberttransform domain or equivalent), and (iii) other possible constraintssuch as bounds of image intensities; and (c) an output, in communicationwith the processing component, for outputting the reconstructed ROI/VOI.27. The system of claim 26, wherein the image norm is an L0 norm or Lpnorm with 1≦p≦2, or a mixed norm.
 28. The system of claim 26, whereinthe imaging component comprises an x-ray imaging system or anotherstraight or approximately straight ray imaging geometry.
 29. The systemof claim 26, wherein the dataset is measured in terms of functions ofweighted sums of unknowns along a bundle of paths that are close to eachother.
 30. The system of claim 28, wherein the imaging component uses asingle or multi-sources.
 31. The system of claim 30, whereinmulti-sources are modulated by coded apertures, share one or moredetectors, and are fired simultaneously or sequentially.
 32. The systemof claim 26, wherein the minimization is with respect to an L0 norm orLp norm with 1≦p≦2, or a mixed norm.
 33. The system of claim 26, whereinthe said sparsifying transform is an invertible transform or frame. 34.The system of claim 33, wherein the invertible transform or frame is awavelet transform, Fourier transform, or another lossless imagecompressive transform or encoding method.
 35. The system of claim 26,wherein the said sparsifying transform is an un-invertible transform.36. The system of claim 35, wherein the un-invertible transform is adiscrete gradient transform, discrete difference transform, orhigh-order transform.
 37. The system of claim 26, wherein the imagingcomponent takes global projection measurements in terms of two or morescout views.
 38. The system of claim 26, wherein the processingcomponent performs: (i) minimizing a data discrepancy; and (ii)minimizing the image norm; wherein the processing component performssteps (i) and (ii) iteratively in an alternating manner.
 39. The systemof claim 38, wherein the imaging component implements an OS-SARTframework.
 40. The system of claim 38, wherein step (i) is implementedin a statistical framework such as using a maximum likelihood (ML)transmission CT approach
 41. The system of claim 40, wherein thestatistical framework is a maximum-likelihood (ML) reconstruction with asignal sparsity model based constraint or penalty.
 42. The system ofclaim 41, wherein the signal sparsity model based constraint is a totalvariation
 43. The system of claim 38, wherein the processing componentperforms a steepest decent search method.
 44. The system of claim 38,wherein the processing component performs a soft-threshold filteringmethod.
 45. The system of claim 44, wherein the soft threshold filteringmethod comprises: (i) computing a sparse transform; (ii) estimating anadapted threshold; (iii) performing soft-threshold filtering; and (iv)performing an inverse sparse transform.
 46. The system of claim 45,wherein the inverse transform of a discrete gradient transform isconstructed as $\begin{matrix}{{f_{i,j}^{k} = {{\lambda_{a}f_{i,j}^{k,a}} + {\lambda_{b}f_{i,j}^{k,b}} + {\lambda_{c}f_{i,j}^{k,c}}}},} \\{f_{i,j}^{k,a} = \left\{ \begin{matrix}{\frac{{2{\overset{\sim}{f}}_{i,j}^{k}} + {\overset{\sim}{f}}_{{i + 1},j}^{k} + {\overset{\sim}{f}}_{i,{j + 1}}^{k}}{4},} & {{{if}\mspace{14mu} d_{i,j}^{k}} < w} \\{{{\overset{\sim}{f}}_{i,j}^{k} - \frac{w\left( {{2{\overset{\sim}{f}}_{i,j}^{k}} - {\overset{\sim}{f}}_{{i + 1},j}^{k} - {\overset{\sim}{f}}_{i,{j + 1}}^{k}} \right)}{4d_{i,j}^{k}}},} & {{{{if}\mspace{14mu} d_{i,j}^{k}} \geq w},}\end{matrix} \right.} \\{f_{i,j}^{k,b} = \left\{ \begin{matrix}{\frac{{\overset{\sim}{f}}_{i,j}^{k} + {\overset{\sim}{f}}_{{i - 1},j}^{k}}{2},} & {{{if}\mspace{14mu} d_{{i - 1},j}^{k}} < w} \\{{{\overset{\sim}{f}}_{i,j}^{k} - \frac{w\left( {{\overset{\sim}{f}}_{i,j}^{k} - {\overset{\sim}{f}}_{{i - 1},j}^{k}} \right)}{2d_{{i - 1},j}^{k}}},} & {{{{if}\mspace{14mu} d_{{i - 1},j}^{k}} \geq w},}\end{matrix} \right.} \\{f_{i,j}^{k,c} = \left\{ \begin{matrix}{\frac{{\overset{\sim}{f}}_{i,j}^{k} + {\overset{\sim}{f}}_{i,{j - 1}}^{k}}{2},} & {{{if}\mspace{14mu} d_{i,{j - 1}}^{k}} < w} \\{{{\overset{\sim}{f}}_{i,j}^{k} - \frac{w\left( {{\overset{\sim}{f}}_{i,j}^{k} - {\overset{\sim}{f}}_{i,{j - 1}}^{k}} \right)}{2d_{i,{j - 1}}^{k}}},} & {{{{if}\mspace{14mu} d_{i,{j - 1}}^{k}} \geq w};}\end{matrix} \right.}\end{matrix}$ where λ_(a)+λ_(b)+λ_(c)=1.
 47. The system of claim 45,wherein the inverse transform of a discrete gradient transform is anynon-linear filter for the reconstructed image to achieve the goal ofsoft-threshold filtering with respect to a given threshold.
 48. Thesystem of claim 45, wherein the inverse transform of a discretedifference transform is constructed as $\begin{matrix}{{f_{i,j}^{k} = {{\lambda_{a}{q\left( {w,{\overset{\sim}{f}}_{i,j}^{k},{\overset{\sim}{f}}_{{i + 1},j}^{k}} \right)}} + {\lambda_{b}{q\left( {w,{\overset{\sim}{f}}_{i,j}^{k},{\overset{\sim}{f}}_{i,{j + 1}}^{k}} \right)}} + {\lambda_{c}{q\left( {w,{\overset{\sim}{f}}_{i,j}^{k},{\overset{\sim}{f}}_{i,{j - 1}}^{k}} \right)}} + {\lambda_{d}{q\left( {w,{\overset{\sim}{f}}_{i,j}^{k},{\overset{\sim}{f}}_{{i - 1},j}^{k}} \right)}}}},} \\{\mspace{79mu} {{q\left( {w,y,z} \right)} = \left\{ \begin{matrix}{\frac{y + z}{2},} & {{{if}\mspace{14mu} {{y - z}}} < w} \\{{y - \frac{w}{2}},} & {{{if}\mspace{14mu} \left( {y - z} \right)} \geq w} \\{{y + \frac{w}{2}},} & {{{{if}\mspace{14mu} \left( {y - z} \right)} \leq {- w}};}\end{matrix} \right.}}\end{matrix}$ where λ_(a)+λ_(b)+λ_(c)+λ_(d)=1.
 49. The system of claim45, wherein the inverse transform of a discrete difference transform isa non-linear filter for the reconstructed image to achieve the goal ofsoft-threshold filtering with respect to a given threshold.
 50. Thesystem of claim 45, wherein the inverse transform of a un-invertiblesparsifying transform is constructed as a non-linear filter for thereconstructed image to achieve the goal of soft-threshold filtering withrespect to a given threshold.